Last updated: 2020-06-08

Checks: 6 1

Knit directory: ~/Research-Local/2019-rnaseq/TCGA-Nigerian-RNAseq/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20190113)

The command set.seed(20190113) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 965fa5a

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/.Rhistory
    Ignored:    code/.DS_Store
    Ignored:    docs/.DS_Store
    Ignored:    docs/figure/.DS_Store
    Ignored:    plots/.DS_Store

Untracked files:
    Untracked:  NigerianTCGArawcountsDeSeq2-pc2.Rmd
    Untracked:  NigerianTCGArawcountslimma-voomDE-PAM50-linc.Rmd
    Untracked:  NigerianTCGArawcountslimma-voomDE-PAM50.Rmd
    Untracked:  _site.yml
    Untracked:  about.Rmd
    Untracked:  docs/figure/NigerianTCGArawcountslimma-voomDE-PAM50.Rmd/
    Untracked:  index.Rmd
    Untracked:  plots/Nanostring-genes-in-RNAseq-results.jpeg

Unstaged changes:
    Modified:   .Rhistory
    Modified:   README.md
    Deleted:    analysis/NigerianTCGArawcountsDeSeq2-pc2.Rmd
    Modified:   analysis/NigerianTCGArawcountsDeSeq2-proteincoding-IHC.Rmd
    Deleted:    analysis/NigerianTCGArawcountslimma-voomDE-PAM50.Rmd
    Deleted:    analysis/_site.yml
    Deleted:    analysis/about.Rmd
    Deleted:    analysis/index.Rmd
    Modified:   code/proteincodingparse.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 9f68f24 Sheila Rajagopal 2019-11-18 GATA3 and TP53 analyses added, holding comparative RNA
Rmd 938bb62 Sheila Rajagopal 2019-09-13 pathway analysis with analysis edits
Rmd 0e0f148 Sheila Rajagopal 2019-09-12 pathway analysis
Rmd 71cf4cd Sheila Rajagopal 2019-09-11 Removed batch 1a samples
Rmd 83061c6 Sheila Rajagopal 2019-08-19 Differential expression with IHC and LOH subanalysis

#Translation from HTSeq raw counts -> Count Matrix I have 66 TCGA patients with whole-genome sequencing data and RNAseq data as well as 49 Nigerian patients with RNA-seq data. Raw counts were initially processed using HTSeq, so HTSeq data is being formatted for use with DESeq2 and limma-voom.

                   sampleConditionIHC
sampleConditionrace Her2 HRpos TNBC
         Nigerian     23     6   20
         TCGA_black    0    10   20
         TCGA_white   10    10   16

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

#Quantile normalization Please refer to: https://parajago.github.io/TCGA-Nigerian-RNAseq/NigerianTCGArawcountsDeSeq2-pc2.html regarding comparison between the Nigerian and TCGA data sets and why quantile normalization under the limma-voom approach was chosen for primary differential expression analysis in any comparisons across populations.

##Data visualization

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

In the raw data with log transformation only, we are able to see that there are two peaks corresponding to the two datasets (Nigerian and TCGA). The quantile normalization demonstrates a PCA that has similar clustering. Only ~20% of the distribution of the data set is explained by the PCA1, 2 of the variables.

##Differential expression setup

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

#DE: 14q LOH in HER2 patients (Nigerian)

designNvsTLOH <- t_norm_countmatrix %>% dplyr::select(condition3)

designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3=="noLOH", 0, as.character(designNvsTLOH$condition3))
designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3=="LOH", 1, as.character(designNvsTLOH$condition3))

designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3==0 | designNvsTLOH$condition3==1, designNvsTLOH$condition3, NA)

designNvsTLOH <- designNvsTLOH %>% subset(is.na(condition3)==FALSE)

designNvsTLOH$noLOH <- ifelse (designNvsTLOH$condition3==0, 1, 0)
designNvsTLOH$LOH <- ifelse (designNvsTLOH$condition3==1, 1, 0)

designNvsTLOH$condition3 <- NULL

mm <- model.matrix(~0+designNvsTLOH$noLOH+designNvsTLOH$LOH)

quantids <- rownames(designNvsTLOH)
rownames(mm) <- quantids
colnames(mm) <- c("noLOH", "LOH")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes

v=voom(d,designNvsTLOH,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(noLOH-LOH, levels=colnames(designNvsTLOH))

fit <- lmFit(v, designNvsTLOH)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)

No significant differential expression was identified via quantile normalization/voom, which is expected as this method can be overly convservative for inter-group differential expression estimation. We have previously validated DESeq2 for inter-Nigerian comparison.

[1] "Intercept"                    "sampleCondition_noLOH_vs_LOH"
MA Plot: Differential expression in Nigerian breast cancer patients based on 14q LOH

[1] 19071
[1] 100

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

We found 100 differentially expressed genes between those patients with 14q LOH and no LOH among the Nigerian patients. We were able to use DESeq2 for this process as this comparison was made only within the Nigerian patients.

#Pathway analysis: 14q LOH in HER2 patients (Nigerians)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

#DE: GATA3 (Nigerian) Among the Nigerian patients, 8 demonstrated GATA3 loss of heterozygosity and 9 demonstrated GATA3 mutations. 32 patients had GATA3 WT.

(In comparison, 4 TCGA black patients had GATA3 LOH and 1 had GATA3 mutation. 4 TCGA white patients had GATA3 LOH and 2 had GATA3 mutation.)

Given the higher numbers, we performed differential expression analysis only on the Nigerian subset.

[1] "Intercept"                           "sampleCondition_GATA3LOH_vs_GATA3wt"
[3] "sampleCondition_GATA3mut_vs_GATA3wt" "batch_HRpos_vs_Her2"                
[5] "batch_TNBC_vs_Her2"                 
MA Plot: Differential expression based on GATA3 LOH status in Nigerians

MA Plot: Differential expression based on GATA3 mutation status in Nigerians

[1] 19346
[1] 313
[1] 19346
[1] 1090

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Because there were so few TCGA paitents with GATA3 changes, we performed a comparison visually to see how mutations changed gene expression of GATA3 specifically. There were 313 gnees differentially expressed with GATA3 LOH and 1090 gnees differentially expressed with GATA3 mutation. GATA3 mutations were associated with high GATA3 expression, whereas LOH demonstrated a range of expression values.

#DE: TP53 (Nigerian) The majority of Nigerian patients had TP53 double hits in their tumors (22); 10 patients had single hits and 17 patients had WT TP53. We therefore performed differential expression analysis to determine if there were any significant additional expression changes between these groups within the Nigerian patients.

sampleTableTP53 <- data.frame(sampleName=gsub(".htseq.counts","",sampleFiles),
                          fileName=sampleFiles,
                          condition1=sampleConditionrace,
                          condition2=sampleTP53,
                          batch=sampleConditionIHC)

sampleTableTP53 <- sampleTableTP53 %>% dplyr::filter(condition1=="Nigerian")

sampleTableTP53$sampleCondition <- sampleTableTP53$condition2
sampleTableTP53$condition1 <- NULL
sampleTableTP53$condition2 <- NULL

ddsHTSeqMFTP53 <- DESeqDataSetFromHTSeqCount(
                                      sampleTable=sampleTableTP53,
                                      directory=FOLDER,
                                      design=~sampleCondition+batch)


ddsHTSeqMFTP53 <- ddsHTSeqMFTP53[rowSums(counts(ddsHTSeqMFTP53)) > 0, ] #Pre-filtering the dataset by removing the rows without any information about gene expression. 981 genes were removed.

ddsHTSeqMFTP53$sampleCondition <- relevel(ddsHTSeqMFTP53$sampleCondition, ref = "TP53wt")

ddsTP53 <- estimateSizeFactors(ddsHTSeqMFTP53)
vsdTP53 <- vst(ddsHTSeqMFTP53, blind=FALSE)

plotPCA(vsdTP53, intgroup=c("sampleCondition", "batch")) + labs(title="PCA of Nigerian patient RNAseq data based on TP53 mutation status")

ddsTP53 <- DESeq(ddsHTSeqMFTP53)
resultsNames(ddsTP53)
[1] "Intercept"                           
[2] "sampleCondition_TP53double_vs_TP53wt"
[3] "sampleCondition_TP53single_vs_TP53wt"
[4] "batch_HRpos_vs_Her2"                 
[5] "batch_TNBC_vs_Her2"                  
cat("MA Plot: Differential expression based on TP53 single-hit status in Nigerians")
MA Plot: Differential expression based on TP53 single-hit status in Nigerians
resTP53singlehit <- lfcShrink(ddsTP53, coef="sampleCondition_TP53single_vs_TP53wt", type="ashr")
DESeq2::plotMA(resTP53singlehit, ylim=c(-10,10), xlim=c(0.1,200))

cat("MA Plot: Differential expression based on TP53 double-hit status in Nigerians")
MA Plot: Differential expression based on TP53 double-hit status in Nigerians
resTP53doublehit <- lfcShrink(ddsTP53, coef="sampleCondition_TP53double_vs_TP53wt", type="ashr")
DESeq2::plotMA(resTP53doublehit, ylim=c(-10,10), xlim=c(0.1,200))

diffTP53single<- results(ddsTP53, contrast=c("sampleCondition", "TP53single", "TP53wt"), pAdjustMethod ="fdr", alpha=fdr)

diffTP53single$foldChange <- NA
row.pos <- which(! is.na(diffTP53single$log2FoldChange) & 
                diffTP53single$log2FoldChange >= 0)
row.neg <- which(! is.na(diffTP53single$log2FoldChange) & 
                diffTP53single$log2FoldChange < 0)
diffTP53single$foldChange[row.pos] <- 2^diffTP53single$log2FoldChange[row.pos]
diffTP53single$foldChange[row.neg] <- -2^((-1) * diffTP53single$log2FoldChange[row.neg])

nrow(diffTP53single)
[1] 19346
diffTP53single <- diffTP53single[(diffTP53single$foldChange > fc | diffTP53single$foldChange < -fc),] 
diffTP53single <- subset(diffTP53single, padj < fdr)
nrow(diffTP53single)
[1] 35
restemp <- lfcShrink(ddsTP53, contrast=c("sampleCondition", "TP53single", "TP53wt"), res = diffTP53single, type="ashr")

restemp$temp <- row.names(restemp)
restemp$temp <- gsub("[.].+", "", restemp$temp)

restemp$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=restemp$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

with(restemp, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between \nbreast cancers in Nigerian patients with and without TP53 single-hit mutations", xlim=c(-60,60), ylim=c(0,60)))
with(subset(restemp, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(restemp, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=symbol, cex=.5))

diffTP53singletable <- as.data.frame(diffTP53single)
diffTP53singletable$temp <- row.names(diffTP53single)
diffTP53singletable$temp <- gsub("[.].+", "", diffTP53singletable$temp)

diffTP53singletable$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53singletable$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

diffTP53singletable$biotype <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53singletable$temp,
                     column="GENEBIOTYPE",
                     keytype="GENEID",           
                     multiVals="first")

diffTP53singletable$chr <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53singletable$temp,
                     column="SEQNAME",
                     keytype="GENEID",           
                     multiVals="first")

diffTP53singletable$locstart <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53singletable$temp,
                     column="GENESEQSTART",
                     keytype="GENEID",
                     multiVals="first")

diffTP53singletable$locend <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53singletable$temp,
                     column="GENESEQEND",
                     keytype="GENEID",
                     multiVals="first")

diffTP53singletable$temp <- NULL

diffTP53singletable <- diffTP53singletable %>% arrange(foldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(foldChange)>1.5)

top_n(diffTP53singletable, 10, foldChange)
top_n(diffTP53singletable, -10, foldChange)
diffTP53singletable$baseMean <- NULL
#write.csv(diffTP53singletable, file = "RNA-DE-Nigerian-TP53-singlehit.csv", row.names =FALSE)


diffTP53double<- results(ddsTP53, contrast=c("sampleCondition", "TP53double", "TP53wt"), pAdjustMethod ="fdr", alpha=fdr)

diffTP53double$foldChange <- NA
row.pos <- which(! is.na(diffTP53double$log2FoldChange) & 
                diffTP53double$log2FoldChange >= 0)
row.neg <- which(! is.na(diffTP53double$log2FoldChange) & 
                diffTP53double$log2FoldChange < 0)
diffTP53double$foldChange[row.pos] <- 2^diffTP53double$log2FoldChange[row.pos]
diffTP53double$foldChange[row.neg] <- -2^((-1) * diffTP53double$log2FoldChange[row.neg])

nrow(diffTP53double)
[1] 19346
diffTP53double <- diffTP53double[(diffTP53double$foldChange > fc | diffTP53double$foldChange < -fc),] 
diffTP53double <- subset(diffTP53double, padj < fdr)
nrow(diffTP53double)
[1] 61
restemp <- lfcShrink(ddsTP53, contrast=c("sampleCondition", "TP53double", "TP53wt"), res = diffTP53double, type="ashr")

restemp$temp <- row.names(restemp)
restemp$temp <- gsub("[.].+", "", restemp$temp)

restemp$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=restemp$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

with(restemp, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between \nbreast cancers in Nigerian patients with and without TP53 double hit mutations", xlim=c(-60,60), ylim=c(0,60)))
with(subset(restemp, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(restemp, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=symbol, cex=.5))

diffTP53doubletable <- as.data.frame(diffTP53double)
diffTP53doubletable$temp <- row.names(diffTP53double)
diffTP53doubletable$temp <- gsub("[.].+", "", diffTP53doubletable$temp)

diffTP53doubletable$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53doubletable$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

diffTP53doubletable$biotype <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53doubletable$temp,
                     column="GENEBIOTYPE",
                     keytype="GENEID",           
                     multiVals="first")

diffTP53doubletable$chr <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53doubletable$temp,
                     column="SEQNAME",
                     keytype="GENEID",           
                     multiVals="first")

diffTP53doubletable$locstart <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53doubletable$temp,
                     column="GENESEQSTART",
                     keytype="GENEID",
                     multiVals="first")

diffTP53doubletable$locend <- mapIds(EnsDb.Hsapiens.v75,
                     keys=diffTP53doubletable$temp,
                     column="GENESEQEND",
                     keytype="GENEID",
                     multiVals="first")

diffTP53doubletable$temp <- NULL

diffTP53doubletable <- diffTP53doubletable %>% arrange(foldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(foldChange)>1.5)

top_n(diffTP53doubletable, 10, foldChange)
top_n(diffTP53doubletable, -10, foldChange)
diffTP53doubletable$baseMean <- NULL
#write.csv(diffTP53doubletable, file = "RNA-DE-Nigerian-TP53-doublehit.csv", row.names =FALSE)

sampleTableTP53 <- data.frame(sampleName=gsub(".htseq.counts","",sampleFiles),
                          fileName=sampleFiles,
                          condition1=sampleConditionrace,
                          condition2=sampleConditionIHC,
                          batch=sampleTP53)

sampleTableTP53$sampleCondition <- paste(sampleTableTP53$condition2, sampleTableTP53$condition1, sep=".")

ddsHTSeqMFTP53 <- DESeqDataSetFromHTSeqCount(
                                      sampleTable=sampleTableTP53,
                                      directory=FOLDER,
                                      design=~sampleCondition+batch)

ddsHTSeqMFTP53 <- ddsHTSeqMFTP53[rowSums(counts(ddsHTSeqMFTP53)) > 0, ] #Pre-filtering the dataset by removing the rows without any information about gene expression

ddsTP53 <- estimateSizeFactors(ddsHTSeqMFTP53)

plotCounts(ddsTP53, gene="ENSG00000141510.11", intgroup=c("sampleCondition", "batch"), main="Distribution of TP53 expression by breast cancer subtype / mutation status in all patients")

plotCounts(ddsTP53, gene="ENSG00000141510.11", intgroup=c("batch"), main="Distribution of TP53 expression by mutation status in all patients")

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

There are 35 differentially expressed genes among the TP53 single changes in the Nigerian cohort and 61 differentially expressed genes among the TP53 double hits in the Nigerian cohort.


sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] parallel  stats4    grid      stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] msigdbr_7.1.1               fgsea_1.10.0               
 [3] Rcpp_1.0.1                  AnnotationHub_2.16.1       
 [5] BiocFileCache_1.8.0         dbplyr_1.4.2               
 [7] Glimma_1.12.0               RColorBrewer_1.1-2         
 [9] preprocessCore_1.46.0       ashr_2.2-32                
[11] ggfortify_0.4.7             calibrate_1.7.2            
[13] MASS_7.3-51.5               sva_3.32.1                 
[15] mgcv_1.8-31                 nlme_3.1-144               
[17] EnsDb.Hsapiens.v75_2.99.0   ensembldb_2.8.0            
[19] AnnotationFilter_1.8.0      GenomicFeatures_1.36.4     
[21] hexbin_1.27.3               stringi_1.4.3              
[23] dplyr_0.8.3                 affy_1.62.0                
[25] checkmate_1.9.3             pathview_1.24.0            
[27] org.Hs.eg.db_3.8.2          AnnotationDbi_1.46.0       
[29] clusterProfiler_3.12.0      pheatmap_1.0.12            
[31] genefilter_1.66.0           vsn_3.52.0                 
[33] RUVSeq_1.18.0               EDASeq_2.18.0              
[35] ShortRead_1.42.0            GenomicAlignments_1.20.0   
[37] Rsamtools_2.0.0             Biostrings_2.52.0          
[39] XVector_0.24.0              DESeq2_1.24.0              
[41] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[43] BiocParallel_1.18.0         matrixStats_0.54.0         
[45] Biobase_2.44.0              GenomicRanges_1.36.0       
[47] GenomeInfoDb_1.20.0         IRanges_2.18.1             
[49] S4Vectors_0.22.0            BiocGenerics_0.30.0        
[51] edgeR_3.26.4                limma_3.40.2               
[53] ggbiplot_0.55               scales_1.0.0               
[55] plyr_1.8.5                  ggplot2_3.2.1              
[57] gplots_3.0.3               

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.1                rtracklayer_1.44.0           
  [3] R.methodsS3_1.7.1             tidyr_1.0.0                  
  [5] acepack_1.4.1                 bit64_0.9-7                  
  [7] knitr_1.28                    aroma.light_3.14.0           
  [9] R.utils_2.8.0                 data.table_1.12.8            
 [11] rpart_4.1-15                  hwriter_1.3.2                
 [13] KEGGREST_1.24.0               RCurl_1.95-4.12              
 [15] doParallel_1.0.14             cowplot_0.9.4                
 [17] RSQLite_2.1.1                 europepmc_0.3                
 [19] bit_1.1-14                    enrichplot_1.4.0             
 [21] xml2_1.3.2                    httpuv_1.5.2                 
 [23] assertthat_0.2.1              viridis_0.5.1                
 [25] xfun_0.7                      hms_0.5.2                    
 [27] evaluate_0.14                 promises_1.0.1               
 [29] progress_1.2.2                caTools_1.17.1.2             
 [31] Rgraphviz_2.28.0              igraph_1.2.4.1               
 [33] DBI_1.0.0                     geneplotter_1.62.0           
 [35] htmlwidgets_1.3               purrr_0.3.3                  
 [37] backports_1.1.4               annotate_1.62.0              
 [39] biomaRt_2.40.0                vctrs_0.2.0                  
 [41] withr_2.1.2                   ggforce_0.2.2                
 [43] triebeard_0.3.0               prettyunits_1.0.2            
 [45] cluster_2.1.0                 DOSE_3.10.1                  
 [47] lazyeval_0.2.2                crayon_1.3.4                 
 [49] labeling_0.3                  pkgconfig_2.0.2              
 [51] tweenr_1.0.1                  ProtGenerics_1.16.0          
 [53] nnet_7.3-12                   rlang_0.4.5                  
 [55] lifecycle_0.1.0               affyio_1.54.0                
 [57] rprojroot_1.3-2               polyclip_1.10-0              
 [59] graph_1.62.0                  Matrix_1.2-18                
 [61] urltools_1.7.3                base64enc_0.1-3              
 [63] whisker_0.3-2                 ggridges_0.5.1               
 [65] png_0.1-7                     viridisLite_0.3.0            
 [67] bitops_1.0-6                  R.oo_1.22.0                  
 [69] KernSmooth_2.23-16            blob_1.1.1                   
 [71] workflowr_1.4.0               mixsqp_0.1-97                
 [73] stringr_1.4.0                 SQUAREM_2017.10-1            
 [75] qvalue_2.16.0                 gridGraphics_0.4-1           
 [77] memoise_1.1.0                 magrittr_1.5                 
 [79] gdata_2.18.0                  zlibbioc_1.30.0              
 [81] compiler_3.6.3                KEGGgraph_1.44.0             
 [83] htmlTable_1.13.1              Formula_1.2-3                
 [85] tidyselect_0.2.5              yaml_2.2.0                   
 [87] GOSemSim_2.10.0               locfit_1.5-9.1               
 [89] latticeExtra_0.6-28           ggrepel_0.8.1                
 [91] fastmatch_1.1-0               tools_3.6.3                  
 [93] rstudioapi_0.11               foreach_1.4.4                
 [95] foreign_0.8-75                git2r_0.26.1                 
 [97] gridExtra_2.3                 farver_1.1.0                 
 [99] ggraph_1.0.2                  digest_0.6.25                
[101] rvcheck_0.1.3                 BiocManager_1.30.10          
[103] shiny_1.3.2                   pscl_1.5.2                   
[105] later_0.8.0                   httr_1.4.1                   
[107] colorspace_1.4-1              XML_3.98-1.20                
[109] fs_1.3.1                      truncnorm_1.0-8              
[111] splines_3.6.3                 ggplotify_0.0.3              
[113] xtable_1.8-4                  jsonlite_1.6.1               
[115] UpSetR_1.4.0                  zeallot_0.1.0                
[117] R6_2.4.0                      Hmisc_4.2-0                  
[119] pillar_1.4.2                  htmltools_0.3.6              
[121] mime_0.7                      glue_1.4.0                   
[123] DESeq_1.36.0                  interactiveDisplayBase_1.22.0
[125] codetools_0.2-16              lattice_0.20-38              
[127] tibble_2.1.3                  curl_4.3                     
[129] gtools_3.8.1                  GO.db_3.8.2                  
[131] survival_3.1-8                rmarkdown_2.1                
[133] munsell_0.5.0                 DO.db_2.9                    
[135] GenomeInfoDbData_1.2.1        iterators_1.0.10             
[137] reshape2_1.4.3                gtable_0.3.0